library(STutility)
## Loading required package: Seurat
## Attaching SeuratObject
## Loading required package: ggplot2
## Warning in rgl.init(initValue, onlyNULL): RGL: unable to open X11 display
## Warning: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'.
## Registered S3 method overwritten by 'imager':
## method from
## plot.imlist
library(corrplot)
## corrplot 0.84 loaded
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(pheatmap)
library(readr)
data = '/home/ivar.karam/presentation/data'
images = '/home/ivar.karam/presentation/images'
spots = '/home/ivar.karam/presentation/spots'
samples <- list.files(path = file.path(data), pattern = 'tsv', full.names = T, recursive = T)
imgs <- list.files(path = file.path(images), pattern = '.jpg', full.names = T, recursive = T)
spotfiles <- list.files(path = file.path(spots), pattern = 'spots.tsv', full.names = T)
Make a data frame consisting of the sample data and meta data
info_table <- data.frame(samples, imgs, spotfiles, stringsAsFactors = F,region = c("A1" ,"A2", "B1" ,"B2", "C1", "C2", "D1", "D2", "E1", "E2", "F1", "F2"), patient = c("A","A", "B", "B","C", "C","D", "D","E", "E","F", "F"))
Read in annotation file to convert ENSEMBL ids to gene symbols when reading in seurat object
ensids <- read.table("/home/ivar.karam/TNBC_new/TNBC_2/genes.tsv", header = T, sep = "\t", stringsAsFactors = F)
se<- InputFromTable(infotable = info_table,
annotation = ensids,
platform = "2k",
transpose = T)
## Using spotfiles to remove spots outside of tissue
## Loading /home/ivar.karam/presentation/data/CN_01Sample_01C1.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_01Sample_02E1.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_05Sample_09D1.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_05Sample_10E2.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_14Sample_27C2.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_14Sample_28E1.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_15Sample_29C1.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_15Sample_30D2.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_39Sample_77D1.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_39Sample_78E1.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_45Sample_89C1.tsv count matrix from a '2k' experiment
## Loading /home/ivar.karam/presentation/data/CN_45Sample_90E1.tsv count matrix from a '2k' experiment
## Using provided annotation table with id.column 'gene_id' and replace column 'gene_id' to convert gene names
##
## ------------- Filtering (not including images based filtering) --------------
## Spots removed: 0
## Genes removed: 3585
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## After filtering the dimensions of the experiment is: [43601 genes, 12573 spots]
Plot distribution of nfeatures and ncounts
Set a threshold
p2 + geom_vline(xintercept = 400, linetype="dashed")
Look at distribution of unique genes/spot and counts/spot for each patient
VlnPlot(object = se, features = c("nFeature_RNA", "nCount_RNA"), group.by = "patient") +NoLegend()
Further explore the spatial distribution of the unique genes/spot
ST.FeaturePlot(se, features = "nFeature_RNA", dark.theme = TRUE, cols = c("dark blue", "cyan", "yellow", "red", "dark red"), ncol = 3)
Quality control of mitochondrial/Ribosomal genes Want to look for variation in these and outliers and ultimately decide whether they should be kept or not as they would affect downstream analysis somewhat
mt.genes <- grep(pattern = "^MT-", x = rownames(se), value = TRUE)
se$percent.mito <- (Matrix::colSums(se@assays$RNA@counts[mt.genes, ])/Matrix::colSums(se@assays$RNA@counts))*100
# Collect all genes coding for ribosomal proteins
rp.genes <- grep(pattern = "^RPL|^RPS", x = rownames(se), value = TRUE)
se$percent.ribo <- (Matrix::colSums(se@assays$RNA@counts[rp.genes, ])/Matrix::colSums(se@assays$RNA@counts))*100
VlnPlot(object = se, features = "percent.mito", group.by = "patient") + NoLegend()
VlnPlot(object = se, features = "percent.ribo", group.by = "patient") + NoLegend()
se.subset <- SubsetSTData(se, expression = nFeature_RNA > 400)
genes <- rownames(se)
keep.genes <- genes[!(grepl("^(MT-|RPL|RPS|MRPL)",genes))]
se.subset <- SubsetSTData(object = se.subset, features = keep.genes)
cat("Spots removed: ", ncol(se) - ncol(se.subset), "\n")
## Spots removed: 96
cat("Genes removed: ", nrow(se) - nrow(se.subset))
## Genes removed: 1054
Upload the images
se.subset <- LoadImages(se.subset, time.resolve = F, verbose = T)
## Loading images for 12 samples:
## Reading /home/ivar.karam/presentation/images/CN_01Sample_01C1_small.jpg for sample 1 ...
## Scaling down sample 1 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_01Sample_02E1_small.jpg for sample 1 ...
## Scaling down sample 2 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_05Sample_09D1_small.jpg for sample 1 ...
## Scaling down sample 3 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_05Sample_10E2_small.jpg for sample 1 ...
## Scaling down sample 4 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_14Sample_27C2_small.jpg for sample 1 ...
## Scaling down sample 5 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_14Sample_28E1_small.jpg for sample 1 ...
## Scaling down sample 6 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_15Sample_29C1_small.jpg for sample 1 ...
## Scaling down sample 7 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_15Sample_30D2_small.jpg for sample 1 ...
## Scaling down sample 8 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_39Sample_77D1_small.jpg for sample 1 ...
## Scaling down sample 9 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_39Sample_78E1_small.jpg for sample 1 ...
## Scaling down sample 10 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_45Sample_89C1_small.jpg for sample 1 ...
## Scaling down sample 11 image from 9523x9523 pixels to 400x400 pixels
## Reading /home/ivar.karam/presentation/images/CN_45Sample_90E1_small.jpg for sample 1 ...
## Scaling down sample 12 image from 9523x9523 pixels to 400x400 pixels
se_split <- lapply(unique(se.subset$patient), function(p) {
SubsetSTData(se.subset, expression = patient %in% p)
})
for (i in 1: length(se_split)){
ImagePlot(se_split[[i]], ncols = 1, method = "raster", type = "raw")
}
Normalize each individual patient
for (i in 1:length(se_split)){
se_split[[i]]<- SCTransform(se_split[[i]], verbose = FALSE, variable.features.rv.th = 1.1, variable.features.n = NULL, return.only.var.genes = FALSE)
}
It could be interesting to look at how the patients compare based on a bulk PCA run. Therefore, we merge al the seurat objects to one, normalize the merged object and deconvolve with PCA
merged_ST <- merge(se_split[[1]], y = c(se_split[2:6]),merge.data = TRUE)
# Normalize
merged_ST <- SCTransform(object = merged_ST, verbose = FALSE, variable.features.rv.th = 1.1, variable.features.n = NULL, return.only.var.genes = FALSE)
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merged_ST <- RunPCA(merged_ST)
merged_ST <- RunUMAP(merged_ST, dims = 1:30)
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
DimPlot(merged_ST, group.by = "region", label = T) + NoAxes() + ggtitle("Spots embedded in UMAP") + NoLegend()
Find spatial patterns of the data with NMF
se_split[[1]] <- RunNMF(se_split[[1]], nfactors = 10)
se_split[[2]] <- RunNMF(se_split[[2]], nfactors = 10)
se_split[[3]] <- RunNMF(se_split[[3]], nfactors = 10)
se_split[[4]] <- RunNMF(se_split[[4]], nfactors = 10)
se_split[[5]] <- RunNMF(se_split[[5]], nfactors = 10)
se_split[[6]] <- RunNMF(se_split[[6]], nfactors = 10)
Look at the driver genes and the distribution of the patterns
driver_genes <- function (se_object){
len_factor <- length(se_object[["NMF"]])
for (i in (1:len_factor)){
print(FactorGeneLoadingPlot(se_object, factor = i, topn = 10, dark.theme = TRUE))
}
}
spatial_distributions <- function(se_object){
cscale <- c("darkblue", "cyan", "yellow", "red", "darkred")
plt<- ST.DimPlot(se_object,
dims = 1:10,
ncol = 3, # Sets the number of columns at dimensions level
grid.ncol = 3, # Sets the number of columns at sample level
reduction = "NMF",
dark.theme = T,
pt.size = 1,
center.zero = F,
cols = cscale)
return (plt)
}
Patient 1
driver_genes(se_split[[1]])
spatial_distributions(se_split[[1]])
Patient 2
driver_genes(se_split[[1]])
spatial_distributions(se_split[[1]])
Patient 3
driver_genes(se_split[[1]])
spatial_distributions(se_split[[1]])
Patient 4
driver_genes(se_split[[1]])
spatial_distributions(se_split[[1]])
Patient 5
driver_genes(se_split[[1]])
spatial_distributions(se_split[[1]])
Patient 6
driver_genes(se_split[[1]])
spatial_distributions(se_split[[1]])
Read the files
library(readr)
# Read in `matrix.mtx`
counts <- Matrix::readMM("/home/ivar.karam/TNBC_new/TNBC_2/sc_data/counts_matrix/matrix.mtx.gz")
# Read in `genes.tsv`
genes <- readr::read_tsv("/home/ivar.karam/TNBC_new/TNBC_2/sc_data/counts_matrix/features.tsv.gz", col_names = FALSE)
## Registered S3 method overwritten by 'cli':
## method from
## print.boxx spatstat.geom
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## X1 = col_character(),
## X2 = col_character(),
## X3 = col_character()
## )
gene_ids <- genes$X1
# Read in `barcodes.tsv`
cell_ids <- read_tsv("/home/ivar.karam/TNBC_new/TNBC_2/sc_data/counts_matrix/barcodes.tsv.gz", col_names = FALSE)$X1
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## X1 = col_character()
## )
Make the column names as the cell IDs and the row names as the gene IDs
rownames(counts) <- gene_ids
colnames(counts) <- cell_ids
Read in the metadata
metadata <- read.table("/home/ivar.karam/TNBC_new/TNBC_2/sc_data/Wu_EMBO_metadata.csv", sep = ",", header = TRUE, stringsAsFactors = FALSE, row.names = 1)
metadata$percent.mito <- as.numeric(metadata$percent.mito)
## Warning: NAs introduced by coercion
metadata$nFeature_RNA <- as.numeric(metadata$nFeature_RNA)
## Warning: NAs introduced by coercion
metadata$nCount_RNA <- as.numeric(metadata$nCount_RNA)
## Warning: NAs introduced by coercion
sapply(metadata, class)
## orig.ident percent.mito patientID nCount_RNA nFeature_RNA
## "character" "numeric" "character" "numeric" "numeric"
## celltype_final
## "character"
Remove the first row
metadata<- metadata[2:24272,]
Check that rownames of meta data matches colnames of gene expression data
check <- all(rownames(metadata) == colnames(counts))
check
## [1] TRUE
Create a seurat object
sc_se <- CreateSeuratObject(counts = counts, meta.data = metadata)
First thing to remove mitochondrial genes and ribosomal genes.
genes <- rownames(sc_se)
keep.genes <- genes[!(grepl("^(MT-|RPL|RPS|MRPL)",genes))]
sc_se.subset <- subset(sc_se, features = keep.genes)
cat("Genes removed are: ", nrow(sc_se) - nrow(sc_se.subset))
## Genes removed are: 161
Secondly, remove bad cells
keep.cells <- sc_se$nFeature_RNA >250
keep.cells <- which(keep.cells)
sc_se.subset <- subset(sc_se.subset, cells = keep.cells)
cat("Cells removed: ", ncol(sc_se) - ncol(sc_se.subset))
## Cells removed: 429
sc_se.subset <- SCTransform(sc_se.subset, verbose = FALSE, variable.features.rv.th = 1.1, variable.features.n = NULL, return.only.var.genes = FALSE)
sc_se.subset <- RunPCA(object = sc_se.subset, verbose = FALSE)
sc_se.subset <- RunUMAP(object = sc_se.subset, verbose = FALSE, dims = 1:30)
DimPlot(sc_se.subset, group.by = "celltype_final", label = T) +NoAxes() + NoLegend()+ ggtitle("Single cells embedded in UMAP")
DimPlot(sc_se.subset, group.by = "celltype_final", label = F) +NoAxes() + ggtitle("Single cells embedded in UMAP")
for (i in 1:length(se_split)){
anchors <- FindTransferAnchors(reference = sc_se.subset, query = se_split[[i]], normalization.method = "SCT",verbose = FALSE)
predictions.assay <- TransferData(anchorset = anchors, refdata = sc_se.subset$celltype_final, prediction.assay = TRUE, weight.reduction = se_split[[i]][["NMF"]], dims = 1:10, verbose = FALSE)
se_split[[i]][["predictions"]] <- predictions.assay
DefaultAssay(se_split[[i]]) <- "predictions"
}
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
Single cell predictions Patient 1
celltypes <- rownames(se_split[[1]][1:20])
col_scale = c("dark blue", "cyan", "yellow", "red", "dark red")
ST.FeaturePlot(object = se_split[[1]], features = c(celltypes), ncol = 4, grid.ncol = 4, cols = col_scale, show.sb = F)
Single cell predictions Patient 2
ST.FeaturePlot(object = se_split[[2]], features = c(celltypes), ncol = 4, grid.ncol = 4, cols = col_scale, show.sb = F)
Single cell predictions Patient 3
ST.FeaturePlot(object = se_split[[3]], features = c(celltypes), ncol = 4, grid.ncol = 4, cols = col_scale, show.sb = F)
Single cell predictions Patient 4
ST.FeaturePlot(object = se_split[[4]], features = c(celltypes), ncol = 4, grid.ncol = 4, cols = col_scale, show.sb = F)
Single cell predictions Patient 5
ST.FeaturePlot(object = se_split[[5]], features = c(celltypes), ncol = 4, grid.ncol = 4, cols = col_scale, show.sb = F)
Single cell predictions Patient 6
ST.FeaturePlot(object = se_split[[6]], features = c(celltypes), ncol = 4, grid.ncol = 4, cols = col_scale, show.sb = F)
cscale <- colorRampPalette(c("darkblue", "white", "darkred"))
for (i in 1: length(se_split)){
data_patient <- GetAssayData(object = se_split[[i]][1:20])
a<-apply(data_patient, 1, function(row) all(row == 0))
values<-which(!a)
patient_values<-data_patient[values,]
patient_cor <- cor(t(patient_values), method='pearson')
corrplot(patient_cor,method="color", col = cscale(200))
}
Change the assay back to SCT
for (i in 1:length(se_split)){
DefaultAssay(se_split[[i]]) <- "SCT"
}
Start with finding neighbors based on the already computed NMF dimensionality reduction
se_split[[1]] <- FindNeighbors(object = se_split[[1]], reduction = "NMF", dims = 1:10)
se_split[[2]] <- FindNeighbors(object = se_split[[2]], reduction = "NMF", dims = 1:10)
se_split[[3]] <- FindNeighbors(object = se_split[[3]], reduction = "NMF", dims = 1:10)
se_split[[4]] <- FindNeighbors(object = se_split[[4]], reduction = "NMF", dims = 1:10)
se_split[[5]] <- FindNeighbors(object = se_split[[5]], reduction = "NMF", dims = 1:10)
se_split[[6]] <- FindNeighbors(object = se_split[[6]], reduction = "NMF", dims = 1:10)
Find clusters
se_split[[1]] <- FindClusters(object = se_split[[1]], verbose = FALSE, resolution = 0.3)
se_split[[2]] <- FindClusters(object = se_split[[2]], verbose = FALSE, resolution = 0.3)
se_split[[3]] <- FindClusters(object = se_split[[3]], verbose = FALSE, resolution = 0.3)
se_split[[4]] <- FindClusters(object = se_split[[4]], verbose = FALSE, resolution = 0.3)
se_split[[5]] <- FindClusters(object = se_split[[5]], verbose = FALSE, resolution = 0.3)
se_split[[6]] <- FindClusters(object = se_split[[6]], verbose = FALSE, resolution = 0.3)
Spatially look at all cluster
library(RColorBrewer)
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
for (i in 1:length(se_split)){
print(ST.FeaturePlot(object = se_split[[i]], features = "seurat_clusters", dark.theme = F, pt.size = 1.5, ncol = 1, show.sb = FALSE, cols = col_vector[c(1:3, 5:7,24,35)]) +ggtitle(""))
}
Look at individual clusters
for (i in 1:length(se_split)){
print(ST.FeaturePlot(object = se_split[[i]], features = "seurat_clusters", pt.size = 1, split.labels = T, indices = 1, show.sb = FALSE, ncol = 3))
print(ST.FeaturePlot(object = se_split[[i]], features = "seurat_clusters", pt.size = 1, split.labels = T, indices = 2, show.sb = FALSE, ncol = 3))
}
Set a threshold of 0.5 average log2 fold change and a p-adjusted value below 0.01
markers1 <- FindAllMarkers(object = se_split[[1]], logfc.threshold = 0.5, return.thresh = 0.01,
only.pos = T, verbose = F)
## For a more efficient implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the limma package
## --------------------------------------------
## install.packages('BiocManager')
## BiocManager::install('limma')
## --------------------------------------------
## After installation of limma, Seurat will automatically use the more
## efficient implementation (no further action necessary).
## This message will be shown once per session
markers2 <- FindAllMarkers(object = se_split[[2]], logfc.threshold = 0.5, return.thresh = 0.01,
only.pos = T, verbose = F)
markers3 <- FindAllMarkers(object = se_split[[3]], logfc.threshold = 0.5, return.thresh = 0.01,
only.pos = T, verbose = F)
markers4 <- FindAllMarkers(object = se_split[[4]], logfc.threshold = 0.5, return.thresh = 0.01,
only.pos = T, verbose = F)
markers5 <- FindAllMarkers(object = se_split[[5]], logfc.threshold = 0.5, return.thresh = 0.01,
only.pos = T, verbose = F)
markers6 <- FindAllMarkers(object = se_split[[6]], logfc.threshold = 0.5, return.thresh = 0.01,
only.pos = T, verbose = F)
all.markers <- list(markers1, markers2, markers3, markers4, markers5, markers6)
names(all.markers) <- c("A", "B", "C", "D", "E", "F")
Now for each patient’s clusters we want to know which genes overlap with every other cluster in every other patient, store that in datalist. Also store a dataframe with all jaccard indices, store that in jaccard_total
datalist <- c()
jaccard_total = data.frame()
for (i in 1:length(all.markers)){ # iterate over each patient data
cluster <- unique(all.markers[[i]]$cluster)
ref <- names(all.markers[i])
rest <- names(all.markers) #!= ref
rest_data <- all.markers[rest] # get the "query" data
for (cl in cluster){ # iterate over each cluster in the reference data
reference_cl <- subset(all.markers[[i]], cluster==cl)
for (j in 1:length(rest_data)){ # iterate over each the query data
query_clusters <- unique(rest_data[[j]]$cluster)
query_name<- names(rest_data[j])
for (q_cl in query_clusters){ # iterate over each cluster in the reference data
query_cl <- subset(rest_data[[j]], cluster==q_cl)
bool_ref <- reference_cl$gene %in% query_cl$gene
bool_query <- query_cl$gene %in% reference_cl$gene
overlap_ref <- which(bool_ref)
overlap_query <- which(bool_query)
genes <- reference_cl$gene[overlap_ref]
pvals_ref <- reference_cl$p_val[overlap_ref]
log2FC_ref <- reference_cl$avg_log2FC[overlap_ref]
pvals_query <- query_cl$p_val[overlap_query]
log2FC_query <- query_cl$avg_log2FC[overlap_query]
df <- data.frame(gene=genes, adj_pval_ref = pvals_ref, avg_log2FC_ref = log2FC_ref,
adj_pval_query = pvals_query, avg_log2FC_query = log2FC_query)
df <- mutate(df, patient_ref= paste0(ref,cl), patient_query=paste0(query_name, q_cl))
data_ls <- list(df)
datalist <- c(datalist, data_ls)
# get jaccard indexes
jaccard_index = length(overlap_ref) / length(unique(c(reference_cl$gene, query_cl$gene)))
j_df <- data.frame(jaccard=jaccard_index, patient_ref= paste0(ref,cl), patient_query=paste0(query_name, q_cl))
jaccard_total <- rbind(jaccard_total,j_df)
}
}
}
}
big_data <- dplyr::bind_rows(datalist)
Vector2<-matrix(jaccard_total$jaccard,nrow=43,byrow=TRUE)
colnames(Vector2) <- unique(jaccard_total$patient_ref)
#Make the 1 values (representing identical clusters compared to each other) as NA
Vector2[Vector2==1] <- NA
Annotate the rows based on single cell labels
v <- c("A0: iCAFs/Plasma cells", "A1: Myoepithelial", "A2: Epithelial luminal mature", "A3: Epithelial-Basal", "A4: myCAFs", "A5: Plasma cells", "A6: Epithelial-Basal",
"B0: Myeloid/B-cells", "B1: Plasma cells/Epithelial-Basal", "B2: Endothelial/dPVL/Unassigned T-cells",
"B3: Myoepithelial/CD4+T-cells/T-regs", "B4: Epithelial-Basal", "B5: iCAFs/Plasma cells/CD8+ T-cells", "B6: Epithelial-Basal/myCAFs", "C0: Epithelial-Basal/Plasma cells", "C1: Epithelial-Basal", "C2: Plasma cells", "C3: dPVL/imPVL/Myoepithelial","C4: Epithelial-Basal/myCAFs", "C5: B-cells/Myeloid/CD4+ T-cells-/T-regs", "C6: Epithelial-Basal", "D0: iCAFs/myCAFs", "D1: Epithelial-Basal", "D2: Epithelial-Basal", "D3: Plasma cells/B-cells/Epithelial-Basal", "D4: Myoepithelial", "D5: Epithelial-Basal", "D6: Myoepithelial/Endothelial cells", "D7: myCAFs", "E0: Endothelial cells/dPVL/imPVL", "E1: Epithelial-Basal", "E2: Epithelial-Basal", "E3: Epithelial-Basal/Epithelial-Basal-Cycling", "E4: Plasma cells/myCAFs/imPVL", "E5: Myoepitelial/Epithelial-Basal", "E6: Myeloid/myCAFs", "E7: Endothelial/Myoepithelial", "F0: iCAFs/Plasma cells", "F1: Endothelial/Myeloid/Plasma cells", "F2: Epithelial-Basal/Endothelial/CD8+ T-cells", "F3: Epithelial-Basal", "F4: Epithelial-Basal/Epithelial-Luminal Mature", "F5: myCAFs/imPVL")
row.names(Vector2) <- v
Make a distance matrix and dendrogram used to group the clusters
distMat <- dist(Vector2, method = "euclidean")
tree <- hclust(d = distMat, method = "complete")
clusters <- cutree(tree, k = 7)
ann <- data.frame(clusters = factor(clusters), row.names = names(clusters))
ann <- ann[tree$order, ,drop = FALSE]
ann
## clusters
## A0: iCAFs/Plasma cells 1
## D7: myCAFs 1
## F5: myCAFs/imPVL 1
## D3: Plasma cells/B-cells/Epithelial-Basal 5
## B3: Myoepithelial/CD4+T-cells/T-regs 5
## E4: Plasma cells/myCAFs/imPVL 5
## B1: Plasma cells/Epithelial-Basal 5
## C2: Plasma cells 5
## B2: Endothelial/dPVL/Unassigned T-cells 5
## B5: iCAFs/Plasma cells/CD8+ T-cells 5
## A6: Epithelial-Basal 5
## B0: Myeloid/B-cells 5
## A2: Epithelial luminal mature 3
## A4: myCAFs 3
## F1: Endothelial/Myeloid/Plasma cells 3
## F2: Epithelial-Basal/Endothelial/CD8+ T-cells 3
## D1: Epithelial-Basal 3
## D5: Epithelial-Basal 3
## D2: Epithelial-Basal 3
## F3: Epithelial-Basal 3
## B6: Epithelial-Basal/myCAFs 3
## E2: Epithelial-Basal 3
## E5: Myoepitelial/Epithelial-Basal 3
## A1: Myoepithelial 2
## A3: Epithelial-Basal 2
## E1: Epithelial-Basal 2
## E3: Epithelial-Basal/Epithelial-Basal-Cycling 2
## F4: Epithelial-Basal/Epithelial-Luminal Mature 2
## C1: Epithelial-Basal 2
## C6: Epithelial-Basal 2
## B4: Epithelial-Basal 2
## C0: Epithelial-Basal/Plasma cells 2
## F0: iCAFs/Plasma cells 6
## D0: iCAFs/myCAFs 6
## E0: Endothelial cells/dPVL/imPVL 6
## E7: Endothelial/Myoepithelial 7
## D4: Myoepithelial 7
## D6: Myoepithelial/Endothelial cells 7
## C4: Epithelial-Basal/myCAFs 4
## C5: B-cells/Myeloid/CD4+ T-cells-/T-regs 4
## C3: dPVL/imPVL/Myoepithelial 4
## A5: Plasma cells 4
## E6: Myeloid/myCAFs 4
pheatmap(Vector2, show_colnames =T, show_rownames = T, border_color = FALSE, annotation_col = ann,color = colorRampPalette(rev(brewer.pal(n = 11, name ="Spectral")))(100))
Pick all genes used to compute the jaccard indices. Here all genes from each cluster will be arranged based on descending log2 fold change
pathway_genes <- function (marker_df){
df_clusters <- c()
for (i in unique(marker_df$cluster)){
m <- subset(marker_df, cluster==i)
m <- arrange(m, desc(avg_log2FC))
up_genes <- m$gene
df_list <- list(up_genes)
df_clusters <- c(df_clusters, df_list)
}
return(df_clusters)
}
m1_genes <- pathway_genes(markers1)
m2_genes <- pathway_genes(markers2)
m3_genes <- pathway_genes(markers3)
m4_genes <- pathway_genes(markers4)
m5_genes <- pathway_genes(markers5)
m6_genes <- pathway_genes(markers6)
Function to perform ORA with gprofiler 2 using GO:BP as source and retrieve the top 3 enriched pathways for that cluster (given there are three below a threshold of 0.01)
library(gprofiler2)
is.not.null <- function(x) !is.null(x)
bp_enrichment_top3 <- function(cluster_genes, marker_df) {
df <- data.frame()
cluster_number <- unique(marker_df$cluster)
for (i in 1:length(cluster_genes)){
enrichment <- gost(query = cluster_genes[[i]], ordered_query = TRUE, organism = 'hsapiens', correction_method = 'fdr', user_threshold = 0.05, source="GO:BP")
if (is.not.null(enrichment$result)){
enrichment$result <- mutate(enrichment$result, cluster = cluster_number[i])
df <- rbind(df, enrichment$result[,c("term_name", "p_value", "cluster")][1:3,])
}
}
log10_pvalue <- -log10(unlist(df$p_value))
df <- cbind(df, log10_pvalue)
df$term_name <- toupper(df$term_name)
return (df)
}
Function to plot the enriched pathways.
bp_plot <- function(data){
plt <- ggplot(data, aes(x = cluster, y = term_name)) +
geom_point(aes(color=term_name, size=log10_pvalue)) +
theme_classic()+
theme(legend.position="right", axis.text = element_text(size=12), axis.title.x = element_text(size=12), axis.title.y = element_blank(), legend.text = element_text(size=12),
legend.title = element_text(size = 12)) +
xlab(label = "Clusters")+
guides(color = FALSE, size=guide_legend("-log10 (FDR p-value)"))
return(plt)
}
m1_bp <- bp_enrichment_top3(m1_genes, markers1)
m2_bp <- bp_enrichment_top3(m2_genes, markers2)
m3_bp <- bp_enrichment_top3(m3_genes, markers3)
m4_bp <- bp_enrichment_top3(m4_genes, markers4)
m5_bp <- bp_enrichment_top3(m5_genes, markers5)
m6_bp <- bp_enrichment_top3(m6_genes, markers6)
gprofiler2::upload_GMT_file("/home/ivar.karam/presentation//h.all.v7.2.symbols.gmt")
Take the same function and alter it so that the organism is specified with the retrieved organism ID generated from uploading the hallmarks GMT file to gprofiler
hallmarks_enrichment <- function(cluster_genes, marker_df) {
df <- data.frame()
cluster_number <- unique(marker_df$cluster)
for (i in 1:length(cluster_genes)){
enrichment <- gost(query = cluster_genes[[i]], ordered_query = TRUE, organism = 'gp__H5Ou_GQmg_g1w', correction_method = 'fdr', user_threshold = 0.05)
if (is.not.null(enrichment$result)){
enrichment$result <- mutate(enrichment$result, cluster = cluster_number[i])
df <- rbind(df, enrichment$result[,c("p_value", "cluster", "term_id")])
}
}
log10_pvalue <- -log10(unlist(df$p_value))
df <- cbind(df, log10_pvalue)
df$term_id <- gsub(pattern = "_", replacement = " ", x = df$term_id)
df <- df %>% rename(term_name = term_id)
return (df)
}
m1_hallmarks <- hallmarks_enrichment(m1_genes, markers1)
m2_hallmarks <- hallmarks_enrichment(m2_genes, markers2)
m3_hallmarks <- hallmarks_enrichment(m3_genes, markers3)
m4_hallmarks <- hallmarks_enrichment(m4_genes, markers4)
m5_hallmarks <- hallmarks_enrichment(m5_genes, markers5)
m6_hallmarks <- hallmarks_enrichment(m6_genes, markers6)
pathway_A <- dplyr::bind_rows(m1_bp, m1_hallmarks)
pathway_B <- dplyr::bind_rows(m2_bp, m2_hallmarks)
pathway_C <- dplyr::bind_rows(m3_bp, m3_hallmarks)
pathway_D <- dplyr::bind_rows(m4_bp, m4_hallmarks)
pathway_E <- dplyr::bind_rows(m5_bp, m5_hallmarks)
pathway_F <- dplyr::bind_rows(m6_bp, m6_hallmarks)
bp_plot(pathway_A)
bp_plot(pathway_B)
bp_plot(pathway_C)
bp_plot(pathway_D)
bp_plot(pathway_E)
bp_plot(pathway_F)
Look at which cluster are enriched for EMT and what their significance is
emt <- data.frame()
all_pathways <- list(pathway_A, pathway_B, pathway_C, pathway_D, pathway_E, pathway_F)
for (i in 1:length(all_pathways)){
patients <- c("A", "B","C", "D", "E","F")
data<-subset(all_pathways[[i]], term_name=="HALLMARK EPITHELIAL MESENCHYMAL TRANSITION")
data <- data %>% mutate(patient=patients[i])
emt <- rbind(emt, data)
}
arrange(emt, desc(log10_pvalue))
## term_name p_value cluster
## 25 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 3.508453e-24 0
## 70 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 5.477837e-16 7
## 58 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 8.208929e-13 4
## 56 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.488217e-11 5
## 19 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 3.859671e-11 0
## 45 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.012890e-10 4
## 83 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 3.687899e-10 6
## 251 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 4.363936e-09 0
## 24 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 7.692645e-08 1
## 47 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.996976e-07 4
## 65 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 6.713240e-06 6
## 38 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 8.376311e-06 2
## 49 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.033578e-05 4
## 59 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 2.594136e-05 5
## 32 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 3.974895e-05 2
## 39 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 9.900979e-05 3
## 96 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 5.495202e-04 7
## 241 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.036339e-03 0
## 54 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.340166e-02 4
## 61 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.725232e-02 5
## 33 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.927848e-02 1
## 46 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 1.938622e-02 4
## 321 HALLMARK EPITHELIAL MESENCHYMAL TRANSITION 4.052505e-02 2
## log10_pvalue patient
## 25 23.454884 D
## 70 15.261391 D
## 58 12.085713 E
## 56 10.827334 A
## 19 10.413450 F
## 45 9.994438 C
## 83 9.433221 E
## 251 8.360122 E
## 24 7.113924 B
## 47 6.699627 A
## 65 5.173068 D
## 38 5.076947 E
## 49 4.985657 D
## 59 4.586007 F
## 32 4.400674 A
## 39 4.004322 C
## 96 3.260016 E
## 241 2.984498 C
## 54 1.872841 F
## 61 1.763152 D
## 33 1.714927 C
## 46 1.712507 B
## 321 1.392276 B
Now annotate the clusters based on enriched pathways
ALL unknown are NOT clearly annotated with either GO:BP or HALLMARKS
To try and remove noise only clusters with a -log10 adjusted p-value equal to or above 5 is referred to as EMT
v <- c("A0: Immune: B-cell/Adipogenesis", "A1: Estrogen response/Respiration", "A2: Glycolysis/Hypoxia", "A3: Growth factor response", "A4: Apoptosis/EMT", "A5: EMT/Interferon", "A6: Unclear", "B0: Interferon", "B1: EMT/Coagulation", "B2: Sterol import", "B3: Immune: Leukocytes", "B4: Secretion", "B5: Unclear", "B6: Neutrophil activation", "C0: Cell adhesion", "C1: Cell adhesion", "C2: Immune: B-cell", "C3: Immune response", "C4: EMT/Apoptosis/Interferon", "C5: Immune: Leukocytes", "C6: Unclear", "D0: EMT", "D1: Keratinization", "D2: Respiration", "D3: Immune: Leukocytes", "D4: Innate immune", "D5: Immune: APC/Cytokine", "D6: EMT/Estrogen response", "D7: EMT/Adipogenesis", "E0: EMT/Defense response", "E1: Respiration", "E2: Hypoxia/EMT", "E3: Splicing", "E4: EMT/Immune response", "E5: Immune: APC/Interferon", "E6: EMT/Immune response", "E7: Immune response" , "F0: EMT/Immune response", "F1: Innate immune/Interferon", "F2: Interferon", "F3: Neutrophil activation", "F4: Lipid metabolism/Estrogen response", "F5: Adipogenesis/Hormone response")
row.names(Vector2) <- v
pheatmap(Vector2, show_colnames =T, show_rownames = T, border_color = FALSE, annotation_col = ann, color = colorRampPalette(rev(brewer.pal(n = 11, name ="Spectral")))(100))
We can then look for overlapping genes between clusters in our groups. This code (as with previous codes) might not be the most aesthetically pleasing, however it does it job (thankfully). Briefly, given an input of clusters and a data frame containing meta data of genes overlapping between clusters (the “big_data” object during jaccard calculations) it checks the overlap between two clusters at the time stores that in a list. Lastly, the code will check for genes that are common in all of those list.
find_overlap <- function (clusters, gene_data){
genes <- c()
for (i in clusters){
temp_data <- which(clusters != i)
temp_data <- clusters[temp_data]
for (j in temp_data){
gene <- subset(gene_data, patient_ref==i & patient_query==j)$gene
data <- list(gene)
genes <- c(genes, data)
}
}
overlap <- Reduce(intersect, genes)
return(overlap)
}
cluster1 <- find_overlap(c("A0", "D7", "F5"), big_data) # adipogenesis related, 3 patients
length(cluster1)
## [1] 20
cluster2 <- find_overlap(c("A1", "A3", "E1", "E3", "F4", "C1", "C6", "B4", "C0"), big_data) # no genes
length(cluster2)
## [1] 0
cluster3 <- find_overlap(c("A2", "A4", "F1", "F2", "D1", "D5", "D2", "F3", "B6", "E2"), big_data)
length(cluster3)
## [1] 0
cluster4 <- find_overlap(c("C4", "C5", "C3", "A5", "E6"), big_data) # T-cell enriched cancer, 23 genes, 5 cluster 3 patients
length(cluster4)
## [1] 23
cluster5 <- find_overlap(c("D3", "B3", "E4", "B1", "C2", "B2", "B5", "A6", "B0"), big_data) # no overlap
length(cluster5)
## [1] 0
cluster6 <- find_overlap(c("F0", "D0", "E0"), big_data) # B-cell enriched tumour 3 patients
length(cluster6)
## [1] 46
cluster7 <- find_overlap(c("E7", "D4", "D6"), big_data) # 2 patients only
length(cluster7)
## [1] 61
all_immune <- find_overlap(c("E7", "D4", "D6", "F0", "D0", "E0", "C4", "C5", "C3", "A5", "E6"), big_data)
all_immune
## [1] "IGFBP4"
-Includes A0, D7, F5
cluster1
## [1] "CDR1-AS" "SAA1" "CFD" "FABP4" "RN7SL2" "MALAT1"
## [7] "GPX3" "ADIPOQ" "ADIRF" "SORBS1" "PLIN1" "ADH1B"
## [13] "PLIN4" "GPD1" "G0S2" "RNU4ATAC" "LRP1" "CCDC80"
## [19] "LIPE" "RARRES2"
lncRNA Related: CDR1-AS
Complement genes: CFD,
Fatty acid related genes: FABP4, adiponectin (ADIPOQ), adipogenesis regulatory factor (ADIRF), PLIN1 + PLIN4 (perilipin-1/4), LIPE (hormone sensitive lipase), LRP1 (LDL Receptor Related Protein 1), RARRES2
Fatty acid metabolism: GPD1, G0S2
Cancer: MALAT1
small non coding RNA: RNU4ATAC
includes “C4”, “C5”, “C3”, “A5”, “E6” of which C4, A5 and E6 are annotated as EMT and C5 and C3 annotated as
cluster4
## [1] "HTRA1" "HLA-A" "FBLN1" "S100A4"
## [5] "GPX1" "XAF1" "SERPINB6" "AKR1A1"
## [9] "HLA-DMB" "IGFBP4" "C3" "RP11-543P15.1"
## [13] "GMFG" "FTH1" "CD52" "UCP2"
## [17] "TRBC1" "HLA-E" "VAMP5" "VWF"
## [21] "TRAC" "CCL5" "HLA-DPB1"
Antigen presenting: HLA-A, HLA-DMB, HLA-E, HLA-DPB1
Macrophage marker: CD52
Interferon-related: XAF1
Complement: C3
Cytokine: CCL5
T-cell associated: TRBC1(T Cell Receptor Beta Constant 1), TRAC (T Cell Receptor Alpha Constant)
cluster6
## [1] "TAGLN" "IGHG1" "AEBP1" "BGN" "PTRF" "MYL9"
## [7] "NEAT1" "MALAT1" "IGHA1" "CST3" "SERPINF1" "IGFBP4"
## [13] "PCOLCE" "COL6A2" "RARRES2" "IGKC" "TRIM56" "NBL1"
## [19] "STAT3" "DPP7" "RNF145" "MZB1" "ENG" "PLEC"
## [25] "ISLR" "NDUFA3" "RRBP1" "FBLN1" "EIF3G" "KPNA6"
## [31] "TACC1" "HSPG2" "CD248" "C1R" "MYADM" "NNMT"
## [37] "IGHG2" "MXRA8" "CCDC85B" "CFD" "TPM2" "THY1"
## [43] "CLDN5" "PTGDS" "EHD2" "EMILIN1"
B-cell enriched, all related with cancer “D0”, “E0”, “F0”
B-cell marker: IGHA1 (Immunoglobulin Heavy Constant Alpha 1), IGKC (Immunoglobulin Kappa Constant), , IGHG1 (Immunoglobulin Heavy Constant Gamma 1, IGHG2
Cancer: MALAT1, emt-related: “BGN” “COL6A2” “TAGLN” “NNMT” “IGFBP4” “MYL9” “PCOLCE” “FBLN1” “TPM2” “THY1”
complement: C1R
HLA-A, HLA-DMB, HLA-E, HLA-DPB1
Macrophage marker: CD52
Here I have also added TRBC2 to see if this gene cou
markers_cluster4 <- c("TRAC", "TRBC1", "HLA-A","CD52", "HLA-E", "HLA-DMB", "HLA-DPB1")
e0_e6<- find_overlap(c("E0", "E6"), big_data)
markers_cluster4 %in% e0_e6
## [1] FALSE FALSE FALSE TRUE TRUE FALSE FALSE
So E6 contains B-cell markers but E0 does not contain T-cell markers (both are annotated as EMT), thus we can infer inter patient tumor heterogeneity based on immune infiltration
f0_e6<- find_overlap(c("F0", "E6"), big_data)
markers_cluster4 %in% f0_e6
## [1] FALSE FALSE FALSE TRUE FALSE FALSE TRUE
d0_e6 <- find_overlap(c("D0", "E6"), big_data)
markers_cluster4 %in% d0_e6
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
None of the clusters contain T cell markers
Check for B-cell markers in group4
b_cell_markers <- c("IGHA1", "IGKC", "IGHG1", "IGHG1")
e0_e6<- find_overlap(c("E0", "E6"), big_data)
b_cell_markers %in% e0_e6
## [1] TRUE TRUE TRUE TRUE
E6 contains B-cell markers
e0_c4<- find_overlap(c("E0", "C4"), big_data)
b_cell_markers %in% e0_c4
## [1] FALSE FALSE FALSE FALSE
e0_c5<- find_overlap(c("E0", "C5"), big_data)
b_cell_markers %in% e0_c5
## [1] FALSE FALSE FALSE FALSE
e0_c3<- find_overlap(c("E0", "C3"), big_data)
b_cell_markers %in% e0_c3
## [1] FALSE FALSE FALSE FALSE
e0_a5<- find_overlap(c("E0", "A5"), big_data)
b_cell_markers %in% e0_a5
## [1] FALSE FALSE FALSE FALSE
Only E6 contains B-cell markers in group 6